Toward Optimal Feature Selection

In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an e cient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm e ectively handles datasets with a very large number of features.

• Type: Conference
• Year: 1996
• Where: ICML
• Authors: Daphne Koller, Mehran Sahami
• Keywords: Approximate Algorithm, Feature Subset Selection, ICML 1996, Machine Learning, Optimal Feature Selection